Currently I am using Genetic Algorithim in MATLAB ('ga' and 'gamultiobj') for my set of fitness functions which are generally the difference between the calculated and experimental values. I am working with chemical kinetics domain and I have derived the reaction rate equations using a conventional kinetic model startegy called Langmuir Hinshelwood Hougen Watson (LHHW) model.
In the GA (both for ga and gamultiobj), I am using the default values with nonlinear constarints and upper and lower bounds for the parameters to be regressed (I have 14 parameters to be regressed with 8 experimental set of data). When I am running the GA, sometimes it gives me values which are comprehensive and sometimes the solutions are really insignificant and far away from the expected results. Therefore, i further added a loop to the script where the GA is called several times and the optimal values returned by the GA is stored in an excel file. I got to know that out of 20 loop runs, only 3-4 runs yield feasible (and comprehensive results) while for the rest of the runs the values yield a really high fitness function value (huge error).
In order to avoid this change in the solutions (that is normal to find in case of optimisation algorithim like GA), i used 'rng('default') but now all the solutions are bizzare with huge erroneous results (high values of fitness function). Therefore, my question is there a way to avoid this problem of non-feasible solutions in certain GA runs of the same fitness function?
Feel free to let me know if there is any more information that you need to better understand my question.
